Dr Liam Turner

2025

• Darwish, R. , Abdelmoty, A. and Turner, L. 2025. Investigating social network structures and interests on X audio spaces. Presented at: 13th International Conference on Complex Networks and their Applications Istanbul, Turkey 10-12 Dec 2024. Published in: Cherifi, H. et al., Complex Networks & Their Applications XIII. Studies in Computational Intelligence Vol. 1187. Springer. , pp.397-408. (10.1007/978-3-031-82431-9_33)
• Darwish, R. , Abdelmoty, A. I. and Turner, L. D. 2025. Analysing social network structures and thematic engagement on X audio spaces. Social Network Analysis and Mining 15 90. (10.1007/s13278-025-01516-w)
• Greatrix, T. et al. 2025. High-confidence labelling of pathology reports using LLM-based unanimous ensembles with limited data. Presented at: International Conference of AI in Healthcare Cambridge 8-10 September 2025. Proceedings of the Artificial Intelligence in Healthcare. Lecture Notes in Computer Science Springer. , pp.381-395. (10.1007/978-3-032-00652-3_27)
• Gul, A. , Turner, L. and Fuentes, C. 2025. Conventions and research challenges in considering trust with socially assistive robots for older adults. Frontiers in Robotics and AI 12 1631206. (10.3389/frobt.2025.1631206)
• Gul, A. , Turner, L. D. and Fuentes, C. 2025. Perspectives from unpaid carers on socially assistive robot interactions in older adult care. Presented at: 16th International Conference on Social Robotics +AI Odense, Denmark 23-26 October 2024. Social Robotics. Vol. 15563.Lecture Notes in Computer Science Singapore: Springer. , pp.105-118. (10.1007/978-981-96-3525-2_9)
• Ironside-Smith, R. et al. 2025. A copula approach to modelling dependency patterns in routine vital sign observation timeliness. Presented at: IEEE International Conference on Healthcare Informatics (ICHI) 2025 Cosenza, Italy 18-25 June 2025. Proceedings of the IEEE 13th International Conference on Healthcare Informatics (ICHI). IEEE. (10.1109/ICHI64645.2025.00044)
• Ironside-Smith, R. et al. 2025. Covariates of individual patient vital sign observation timeliness in hospital wards. Presented at: IEEE International Conference on Healthcare Informatics (ICHI) 2025 Cosenza, Italy 18-25 June 2025. Proceedings of the 13th International Conference on Healthcare Informatics. IEEE. , pp.325-335. (10.1109/ICHI64645.2025.00045)
• Siddique, Z. , Turner, L. and Espinosa-Anke, L. 2025. Dialz: A Python toolkit for steering vectors. Presented at: The 63rd Annual Meeting of the Association for Computational Linguistics Vienna, Austria 27 July - 1 August 2025. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). Vol. 3.Vienna, Austria: Association for Computational Linguistics. , pp.363-375. (10.18653/v1/2025.acl-demo.35)

2024

• Greatrix, T. et al. 2024. Can large language models create new knowledge for spatial reasoning tasks?. [Online].arXiv: Cornell University. Available at: https://arxiv.org/abs/2405.14379.
• Gul, A. , Turner, L. and Fuentes, C. 2024. An exploratory analysis of trust in socially assistive robot interactions with unpaid carers of older adults. Presented at: IEEE RO-MAN 2024 Pasadena, CA, USA 26-30 August 2024. 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE. , pp.1630-1637. (10.1109/RO-MAN60168.2024.10731201)
• Ironside-Smith, R. et al. 2024. Motif discovery in hospital ward vital signs observation networks. Network Modeling Analysis in Health Informatics and Bioinformatics 13 55. (10.1007/s13721-024-00490-1)
• Siddique, Z. , Turner, L. and Espinosa-Anke, L. 2024. Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models. Presented at: The 2024 Conference on Empirical Methods in Natural Language Processing Miami, FL, USA 12-16 November 2024. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. , pp.18601-18619. (10.18653/v1/2024.emnlp-main.1035)

2022

• Ashford, J. et al. 2022. Understanding the characteristics of COVID-19 misinformation communities through graphlet analysis. Online Social Networks and Media 27 100178. (10.1016/j.osnem.2021.100178)
• Friedrichs, B. , Turner, L. D. and Allen, S. M. 2022. Utilising the co-occurrence of user interface interactions as a risk indicator for smartphone addiction. Pervasive and Mobile Computing 86 101677. (10.1016/j.pmcj.2022.101677)
• Noe, B. et al. 2022. Temporal patterns in vital sign recording within and across general hospital wards. Resuscitation Plus 10 100247. (10.1016/j.resplu.2022.100247)
• Whitaker, R. M. et al. 2022. The coevolution of social networks and cognitive dissonance. IEEE Transactions on Computational Social Systems 9 (2), pp.376-393. (10.1109/TCSS.2021.3090833)

2021

• Davies, C. et al. 2021. Multi-scale user migration on Reddit. Presented at: Workshop on Cyber Social Threats at the 15th International AAAI Conference on Web and Social Media (ICWSM 2021) Virtual 07 June 2021. AAAI(10.36190/2021.13)
• Friedrichs, B. , Turner, L. and Allen, S. 2021. Discovering types of smartphone usage sessions from user-app interactions. Presented at: 5th International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS 2021) Kassel, Germany 22-26 March 2021. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE. , pp.459-464. (10.1109/PERCOMWORKSHOPS51409.2021.9431034)
• Hudson, L. et al. 2021. The centrality of edges based on their role in induced triads. Presented at: 2021 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Virtual Event, Netherlands 8-11 November 2021. Published in: Coscia, M. , Cuzzocrea, A. and Shu, K. eds. Proceedings of ASONAM '21. ACM. , pp.325-332. (10.1145/3487351)
• Turalska, M. et al., 2021. Optimizing the efficiency of collective decision making in groups. Presented at: SPIE Defense + Commercial Sensing Virtual (FL, United States) 12-14 April 2021. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III. Vol. 11746.International Society for Optics and Photonics(10.1117/12.2587886)

2020

• Ashford, J. et al. 2020. Assessing temporal and spatial features in detecting disruptive users on Reddit. Presented at: International Conference on Advances in Social Networks Analysis and Mining (ASONAM) The Hague, Netherlands 7-10 December 2020. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining. IEEE/ACM. , pp.892-896. (10.1109/ASONAM49781.2020.9381426)
• Morris, R. L. et al. 2020. Breadth verses depth: the impact of tree structure on cultural influence. Presented at: 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS) Washington DC, USA 19-21 October 2020. Social, Cultural, and Behavioral Modeling: 13th International Conference, SBP-BRiMS 2020, Washington, DC, USA, October 18–21, 2020, Proceedings. Lecture Notes in Computer Science/Information Systems and Applications, incl. Internet/Web, and HCI Vol. 12268. Springer Verlag. (10.1007/978-3-030-61255-9_9)
• Tu, K. et al., 2020. gl2vec: Learning feature representation using graphlets for directed networks. Presented at: 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019) Vancouver, BC, Canada 27-30 August 2019. Proceedings of ASONAM 19. New York: Association for Computing Machinery. , pp.216-221. (10.1145/3341161.3342908)

2019

• Ashford, J. et al. 2019. Understanding the signature of controversial Wikipedia articles through motifs in editor revision networks. Presented at: The Web Conference 2019 San Francisco, CA, USA 13-17 May 2019.
• Loizides, F. et al. 2019. Crowdsourcing real world feedback for human computer interaction education. In: Khan, V. J. et al., Macrotask Crowdsourcing: Engaging the Crowds to Address Complex Problems. Human–Computer Interaction Series Springer. , pp.233-252. (10.1007/978-3-030-12334-5_9)
• Loizides, F. et al. 2019. MyCompanion: A digital social companion for assisted living. Presented at: INTERACT 2019 Paphos, Cyprus 2–6 Sep 2019. Published in: Lamas, D. et al., Human-Computer Interaction: INTERACT 2019, Part IV. Vol. 11749.Lecture Notes in Computer Science Cham, Switzerland: Springer Verlag. , pp.649-653. (10.1007/978-3-030-29390-1_55)
• Morris, R. et al. 2019. The impact of peer pressure: extending Axelrod’s model on cultural polarisation. Presented at: 2019 IEEE International Conference on Cognitive Computing (ICCC 2019) MIlan, Italy 8-13 July 2019. IEEE International Conference on Cognitive Computing (ICCC). IEEE. , pp.114-121. (10.1109/ICCC.2019.00030)
• Noe, B. , Turner, L. and Whitaker, R. 2019. Smartphone interaction and survey data as predictors of Snapchat usage. Presented at: UbiComp 2019 London, UK 9-10 Sept 2019. Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. , pp.438-445. (10.1145/3341162.3349298)
• Noe, B. et al. 2019. Identifying indicators of smartphone addiction through user-app interaction. Computers in Human Behavior 99 , pp.56-65. (10.1016/j.chb.2019.04.023)
• Turner, L. , Allen, S. and Whitaker, R. 2019. The influence of concurrent mobile notifications on individual responses. International Journal of Human-Computer Studies 132 , pp.70-80. (10.1016/j.ijhcs.2019.07.011)
• Turner, L. D. et al. 2019. Evidence to support common application switching behaviour on smartphones. Royal Society Open Science 6 (3) 190018. (10.1098/rsos.190018)

2018

• Bellamy, R. et al., 2018. A computational framework for modelling inter-group behaviour using psychological theory. Presented at: SPIE Defense + Security 2018 Orlando, FL, USA 15-18 April 2018. Proceedings Volume 10653, Next-Generation Analyst VI. Vol. 10653.International Society for Optics and Photonics. , pp.106530G. (10.1117/12.2309621)
• Braines, D. et al., 2018. The role of motifs in understanding behavior in social and engineered networks. Presented at: Next Generation Analyst IV at SPIE DSS Orlando, Florida 15-19 Apr 2018.
• Tu, K. et al., 2018. Classifying types of network communities using motifs. Presented at: Machine Learning, Data Analytics and Modeling - DATAM'18 at CCS'18 Thessaloniki, Greece 23-28 September 2018.
• Tu, K. et al., 2018. Network classification in temporal networks using motifs. Presented at: 3rd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (AALTD'18) Dublin, Ireland 10-14 September 2018.

2017

• Eshghi, S. et al., 2017. Mathematical models for social group behavior. Presented at: DAIS 2017 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations San Francisco, CA, USA 4-8 August 2017. Proceedings of the DAIS 2017 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations, San Francisco, CA, USA, 4-8 August 2017. San Francisco: IEEE. , pp.1-6. (10.1109/UIC-ATC.2017.8397423)
• Eshghi, S. et al., 2017. Stability and fracture of social groups. Presented at: 55th Annual Allerton Conference on Communication, Control, and Computing Monticello, IL, USA 3-6 October 2017. Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing. IEEE. , pp.478-485. (doi.org/10.1109/ALLERTON.2017.8262776)
• Noe, B. et al. 2017. Timing rather than user traits mediates mood sampling on smartphones. BMC Research Notes 10 481. (10.1186/s13104-017-2808-1)
• Turner, L. et al. 2017. Parameterising the dynamics of inter-group conflict from real world data. Presented at: DAIS 2017 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations San Francisco, CA, USA 4-8 August 2017. Proceedings of the DAIS 2017 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations, San Francisco, CA, USA, 4-8 August 2017. IEEE. , pp.1-6. (10.1109/UIC-ATC.2017.8397421)
• Turner, L. D. 2017. Decomposing responses to mobile notifications. PhD Thesis , Cardiff University.
• Turner, L. D. , Allen, S. M. and Whitaker, R. M. 2017. Reachable but not receptive: enhancing smartphone interruptibility prediction by modelling the extent of user engagement with notifications. Pervasive and Mobile Computing 40 , pp.480-494. (10.1016/j.pmcj.2017.01.011)
• Whitaker, R. M. et al. 2017. Intra-group tension under inter-group conflict: a generative model using group social norms and identity. Presented at: 8th International Conference on Applied Human Factors and Ergonomics Los Angeles, CA, USA 17-21 July 2017. Proceedings of the 8th International Conference on Applied Human Factors and Ergonomics, Los Angeles, CA, USA, 17-21 July 2017. Vol. 610.Springer. , pp.167–179. (10.1007/978-3-319-60747-4_16)

2015

• Turner, L. D. , Allen, S. M. and Whitaker, R. M. 2015. Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field. Presented at: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Osaka, Japan 7-11 September 2015. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA: ACM. (10.1145/2750858.2807514)
• Turner, L. D. , Allen, S. M. and Whitaker, R. M. 2015. Push or delay? Decomposing Smartphone notification response behaviour. Presented at: Sixth International Workshop on Human Behavior Understanding Osaka, Japan 8 September 2015. Human Behavior Understanding. Lecture Notes in Computer Science Berlin: Springer. , pp.69-83. (10.1007/978-3-319-24195-1_6)